Enterprises that deploy analytics to obtain deep insights boost the odds for success, but ones that stumble may find their organization reeling or even failing.

But Oberweis doesn't stop there. It also uses analytics to better understand customer acquisition, retention and attribution models. This includes which channels and approaches work best for different sets of customers.

"For years, we had engaged in telemarketing, and it worked well," Bedford explains. "But with the advent of 'Do Not Disturb' and changes in society, that withered." At the same time, existing direct- mail strategies had not worked particularly well, and door-to-door sales efforts had become expensive and resource-intensive.

As a result, business leaders identified key factors and variables, and then began A/B testing different approaches. The end result? The company obtained results—sometimes ones that were counterintuitive—that led to far more effective direct-mail initiatives.

Bedford says the company plans to expand the use of big data and analytics into operations and other areas over the next couple of years. This includes a far more sophisticated approach to inventory management.

"We are developing analytics models that allow us to understand the business in ways that weren't possible in the past," he reports. "We are able to cut through the complexity of the business and gain a competitive advantage."

Embracing Transformation

The challenges surrounding big data and analytics aren't getting any easier to resolve. As sources for raw data grow—including through the Internet of things (IoT)—organizations must develop a viable strategy and put the right tools and technologies in place.

EY's Schlesinger points out that hardware and infrastructure are an essential foundation, including open-source components and solutions such as Hadoop and Spark that support data management and sharing. But there's also a need to understand data sources in a deeper and broader way. This includes legacy systems, public sources and new data generators, which might include beacons, sensors and crowdsourced data platforms that rely on smartphones.

Ultimately, Schlesinger says, it's important to treat data as a strategic program and establish a long-term road map. "It's about identifying the right data, discovering and acquiring it, storing it so that it's accessible when it needs to be used, and integrating and organizing it for maximum value," he explains.

While data scientists who can write algorithms are invaluable for framing the strategic direction of a program and building functionality, savvy data analysts are also crucial for success.

"You really need people who understand how to use data sets and find value-added insights," Schlesinger advises. In addition, he says that it's essential to view data in a holistic way and adopt a lifecycle approach tied into Master Data Management (MDM) and data governance.

Capgemini's Belliappa says that a growing level of business disruption translates into a need to analyze data in entirely new and sometimes unfamiliar ways. Over the coming years, almost every industry will face new threats that could revolutionize business and industry.

"Big data and analytics are fundamental to building a culture that embraces innovation and identifies opportunities," he says. "Putting the concept to work means building a modern data architecture, but also having the skills and knowledge to take advantage of the disruption and transform it into a competitive advantage."